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Instructor

Saed Sayad

Instructor is a pioneer researcher in real time data mining, the inventor of Real Time Learning Machine (RTLM), an adjunct Professor at the University of Toronto, and has been presenting a popular graduate data mining course since 2001. He has more than 20 years of experience in data mining, statistics and artificial intelligence and designed, developed and deployed many business and scientific applications of predictive modeling.

Duration: 3h 31m

Course Description

Data Science is about explaining the past and predicting the future by means of data analysis. Data Science is a multi-disciplinary field which combines statistics, machine learning, artificial intelligence and database technology. This course provides the essential concepts and principles in data science. Students learns commonly used classification algorithms and how to use those algorithms to solve real world problems.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

It would be very helpful if students have a basic knowledge of R programming.

Who should take this course? Who should not?

Everyone with basic knowledge of statistics and math can take this course.

Curriculum

Module 1: Classification Algorithms

02:10:30

Lecture 1
Classification - Basic Methods

21:07

Students will learn about ZeroR, OneR and Naive Bayesian classification algorithms

Lecture 2
Classification - Decision Tree

23:38

Decision tree is the most used classification algorithm. Decision tree builds classification or regression models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes.

Lecture 3
Classification - Linear Discriminant Analysis

11:03

Linear Discriminant Analysis (LDA) is based upon the concept of searching for a linear combination of the variables that best separates two classes. LDA is originally developed in 1936 by R. A. Fisher. It is simple, mathematically robust and often produces models whose accuracy is as good as more complex methods.

Lecture 4
Classification - Logistic Regression

17:58

Logistic Regression is a classification model which predicts the probability of an outcome that can only have two values (e.g., binary), Logistic regression produces a logistic curve, which is limited to values between 0 and 1. Logistic regression is similar to a linear regression, but the curve is constructed using the natural logarithm of the “odds” of the target variable, rather than the probability. Moreover, the predictors do not have to be normally distributed or have equal variance in each group.

Lecture 5
Classification - K Nearest Neighbors

14:54

K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions). KNN has been used in statistical estimation and pattern recognition already in the beginning of 1970’s as a non-parametric technique.

Lecture 6
Classification - Artificial Neural Netwrok

12:08

An artificial neutral network (ANN) is a system that is based on the biological neural network, such as the brain. The ANN attempts to recreate the computational mirror of the biological neural network, although it is not comparable since the number and complexity of neurons and the used in a biological neural network is many times more than those in an artificial neutral network.

Lecture 7
Classification - Artificial Neural Network Demo

10:25

Lecture 8
Classification - Model Evaluation

19:17

Model Evaluation is an integral part of the model development process. It helps to find the best model that represents our data and how well the chosen model will work in the future. Evaluating model performance with the data used for training is not acceptable in data mining because it can easily generate overoptimistic and overfitted models. There are two methods of evaluating models in data mining, Hold-Out and Cross-Validation. To avoid overfitting, both methods use a test set (not seen by the model) to evaluate model performance.

Module 2: Calssification - Sample Project

01:20:37

Lecture 9
Classification - Sample Project Part 1/4

24:58

Students will learn how to build a classification model to predict the probability of default for small businesses.

Lecture 10
Classification - Sample Project Part 2/4

11:42

The second part of the sample project is about Bivariate Data Exploration. Students learn how to use different visualization methods to demonstrate the relationship between categorical and numerical variables.

Lecture 11
Classification - Sample Project Part 3/4

16:55

The third part of the sample project is about building classification models such as ZeroR, OneR, Bayesian, Decision Tree and more.

Lecture 12
Classification - Sample Project Part 4/4

27:02

The forth part is about models evaluation using confusion matrix, lift or gain chart, and ROC chart. Students also learn how to deploy a classification model.

Reviews

10 Reviews

Kevin B

December, 2016

Fantastic course. This course could easily be have been priced at $149 (priced at $49) with the amount of content covered. In other words, you are really gaining a tremendous amount of knowledge from the course. The instructor clearly demonstrates step-by-step how to master classification models from defining to deployment of those models. Would high recommend.

William E

May, 2017

An excellent course. This Classification Models course provides the foundational knowledge to use classification models to create business insights. It teaches a systematic approach for building classification models from an input data set. Examples include decision tree classifiers, rule-based classifiers, neural networks, support vector machines, and naıve Bayes classifiers. The course made it easy for me to learn the commonly used classification algorithms and how to use those algorithms to solve business problems from the easy-to-learn well-developed and relevant modules developed for the course.

Josiah P

May, 2017

The course felt interesting with live examples given by the instructor. Since it is online self- paced course, you can learn the course at your own pace so that you get enough time to assimilate what you learned and implement the knowledge in solving your problems.

Lars K

May, 2017

The course is very interactive with learning examples and sample project. The project building exercise makes us practically learn how to build customized models useful and suitable to our business insights.

Yadong Y

July, 2017

The program taught me to understand the generally applied classification algorithms. It showed how to do those algorithms to resolve business issues from the easy-to-understand and fully accounted for appropriate modules built on for the study. I find it a desirable study.

Tony O

July, 2017

The course is very much interactive with studying models and test project. The practice of project construction gives us an understanding as to how to set up customized models appropriate and relevant to our commerce visions.

Cesar H

July, 2017

The course is engaging with real illustrations given out by the guide. As an online self-paced program, you can pick up the course at your own speed to have plenty time to absorb what you learn and execute the principles in resolving your issues.

Melissa S

July, 2017

This course is essential learning for the field of Classification Models and dealing with classification models to make business judgments. It shows a methodical way for bringing out classification models from a raw data value. Instances such as naıve Bayes classifiers, decision tree classifiers, neural networks, rule-based classifiers, back vector machines are all included.

Henk C

October, 2017

I'd recommend this course to anyone who is looking to educate him or herself on classification models. What I loved most about the course was the flow of classification methods from a model with no attribute to a model with one attribute and finally models with all attributes.

Mike L

October, 2017

I learned the fundamentals of classification algorithms in an easy step-by-step method. The videos on how-to use R and the real dataset were extremely helpful. Often when participating in a course or other educational programs, I feel as if there is no real connection between the theorie and the real world. However, by using real datasets the lecturer clearly showed how to use all the theory in practice!

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